Automatic Control of Distributed Systems Based on State Prediction Methods

Distributed systems have been developing rapidly in the past few years and their automatic control is a real challenge being a very active research field. In order to assure the load balancing and to optimize the resource utilization, a distributed system is using different software components, such as management tools, schedulers or monitoring tools. Considering the prediction of future behavior of distributed systems resources can offer better results in optimization and control. This paper proposes a state prediction algorithm based on neural networks using a genetic algorithm for initialization. The algorithm combines the advantages of the neural networks with the advantages of a dynamically constructed architecture and the very good results offered by a genetic algorithm. The prediction system includes the MonALISA monitoring system that collects information about the current status of the available resources in a distributed system. The algorithm is used in order to predict the next value or the next interval of values for parameters such as load, free memory or network bandwidth. The comparison between proposed algorithm and the classical prediction methods highlights the obtained improvements referring to the decreasing of the prediction errors.